Research and Application of Deep Learning Technology in Prefabricated Buildings
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Keywords

Deep learning
Prefabricated building
Application

DOI

10.26689/jwa.v8i2.6920

Submitted : 2024-04-27
Accepted : 2024-05-12
Published : 2024-05-27

Abstract

Prefabricated buildings have become an important development trend in the field of modern architecture because of their high efficiency and environmental friendliness. Artificial intelligence and deep learning technology have been increasingly applied in prefabricated buildings. Deep learning technology provides comprehensive optimization of building design, construction, quality control, and cost and schedule management through the learning and analysis of large amounts of data. This paper aims to explore the application of deep learning technology in prefabricated buildings, analyze the revolutionary changes it brings in different stages, and discuss the problems faced when implementing this technology.

References

Sun Y, 2022, Research on Intelligent Monitoring Method for Safety of Prefabricated Building Components Hoisting, dissertation, Tsinghua University.

Xie J, 2023, Research and Application of Defect Detection Method for Prefabricated Building Components, dissertation, Anhui University.

Sui T, 2022, Research on Key Technologies of Location of Prefabricated Buildings by Close-Range Photogrammetry, dissertation, Hefei University of Technology.

Li P, 2021, Research on Rebar Avoidance of Fabricated Beam-to-Column Joints Based on Deep Reinforcement Learning, dissertation, Hebei University of Science and Technology.

Liu B, 2019, Application of Cutting-Edge Technologies such as Artificial Intelligence Internet of Things in Prefabricated Buildings. Windows and Doors, 2019(16): 250–251.

Huang X, 2023, Research on Deep Integration Development Strategy of BIM Technology and Prefabricated Buildings. Real Estate World, 2023(2): 166–168.

Wang C, 2023, Research on Automatic Damage Screening Technology of High-Rise Building Curtain Wall Based on Deep Learning AI. Journal of Tianjin Vocational Colleges, 25(4): 29–36.

Wang W, Zhang G, Wu K, et al., 2022, Deep Learning Technology Realization of Three-Dimensional Architectural Model Monomeric. Bulletin of Surveying and Mapping, 2022(12): 14–18 + 23.

Yan L, Tu H, Wang D, et al., 2022, Application of Building Identification Technology Based On Deep Learning in Urban Physical Examination. Shanghai Urban Planning Review, 2022(1): 39–46.

Liu H, Zhang C, Ge Y, et al., 2022, Building Extraction by Multi-Scale Feature Fusion in Deep Learning. Geospatial Information, 20(2): 97–100.

Zhang C, Tan R, Song C, et al., 2023, Research on Automatic Classification and Quantitative Measurement of Residential Buildings Based on Deep Learning. Journal of Southwest Normal University (Natural Science Edition), 48(6): 1–11.

Chen X, Tian Q, Yi P, 2023, Architecture Extraction from High-Resolution Remote Sensing Images Based on Deep Learning. World Nuclear Geology, 40(1): 81–88.

Shi W, Li B, You P, et al., 2023, Optimization Strategy of Building Energy System Based on Deep Reinforcement Learning. Electric Power of China, 56(6): 114–122.

Wang J, 2023, Research on Building Extraction from High-Resolution Remote Sensing Image Based on Deep Learning. Journal of Shijiazhuang Railway Technical College, 22(2): 53–57.

Fu Q, Wu S, Dai D, et al., 2020, A Building Energy Consumption Prediction Method Based on Transfer Deep Reinforcement Learning. Applied Research of Computers, 37(S01): 92–94.

Sun J, 2020, Discussion on Deep Integration Strategy Based on BIM Technology and Prefabricated Buildings. Building and Budget, 2020(7): 5–8.

Chen D, Liu Y, He X, et al., 2023, Application Research of BIM Technology in Construction Process Management of Prefabricated Buildings. Science, Technology and Innovation, 2023(18): 173–175.

Hou C, 2023, Application of Prefabricated Construction Technology Under the Background of Green Building. Henan Building Materials, 2023(9): 39–41.